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Add schedulers request cancellation tests
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| #!/usr/bin/env python3 | ||
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| # Copyright 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. | ||
| # | ||
| # Redistribution and use in source and binary forms, with or without | ||
| # modification, are permitted provided that the following conditions | ||
| # are met: | ||
| # * Redistributions of source code must retain the above copyright | ||
| # notice, this list of conditions and the following disclaimer. | ||
| # * Redistributions in binary form must reproduce the above copyright | ||
| # notice, this list of conditions and the following disclaimer in the | ||
| # documentation and/or other materials provided with the distribution. | ||
| # * Neither the name of NVIDIA CORPORATION nor the names of its | ||
| # contributors may be used to endorse or promote products derived | ||
| # from this software without specific prior written permission. | ||
| # | ||
| # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY | ||
| # EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | ||
| # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR | ||
| # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR | ||
| # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, | ||
| # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, | ||
| # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR | ||
| # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY | ||
| # OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT | ||
| # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | ||
| # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
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| import concurrent.futures | ||
| import time | ||
| import unittest | ||
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| import numpy as np | ||
| import tritonclient.grpc as grpcclient | ||
| from tritonclient.utils import InferenceServerException | ||
|
Check notice Code scanning / CodeQL Unused import Note
Import of 'InferenceServerException' is not used.
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| class TestScheduler(unittest.TestCase): | ||
| def setUp(self): | ||
| # Initialize client | ||
| self._triton = grpcclient.InferenceServerClient("localhost:8001") | ||
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| def _get_inputs(self, batch_size): | ||
| self.assertIsInstance(batch_size, int) | ||
| self.assertGreater(batch_size, 0) | ||
| shape = [batch_size, 8] | ||
| inputs = [grpcclient.InferInput("INPUT0", shape, "FP32")] | ||
| inputs[0].set_data_from_numpy(np.ones(shape, dtype=np.float32)) | ||
| return inputs | ||
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| def _generate_callback_and_response_pair(self): | ||
| response = {"responded": False, "result": None, "error": None} | ||
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| def callback(result, error): | ||
| response["responded"] = True | ||
| response["result"] = result | ||
| response["error"] = error | ||
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| return callback, response | ||
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| def _assert_response_is_cancelled(self, response): | ||
| self.assertTrue(response["responded"]) | ||
| self.assertEqual(response["result"], None) | ||
| # FIXME: properly import the error and assert after finalizing client change | ||
| self.assertEqual( | ||
| str(type(response["error"])), | ||
| "<class 'tritonclient.grpc._utils.CancelledError'>", | ||
| ) | ||
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| # Test queued requests on dynamic batch scheduler can be cancelled | ||
| def test_dynamic_batch_scheduler_request_cancellation(self): | ||
| model_name = "dynamic_batch" | ||
| self._triton.load_model(model_name) | ||
| with concurrent.futures.ThreadPoolExecutor() as pool: | ||
| # Saturate the 2 batch slots on the model of 1 instance | ||
| saturate_thread_1 = pool.submit( | ||
| self._triton.infer, model_name, self._get_inputs(batch_size=1) | ||
| ) | ||
| saturate_thread_2 = pool.submit( | ||
| self._triton.infer, model_name, self._get_inputs(batch_size=1) | ||
| ) | ||
| time.sleep(2) # ensure the slots are filled | ||
| # The next request should be queued | ||
| callback, response = self._generate_callback_and_response_pair() | ||
| queue_future = self._triton.async_infer( | ||
| model_name, self._get_inputs(batch_size=1), callback | ||
| ) | ||
| time.sleep(2) # ensure the request is queued | ||
| self.assertFalse(response["responded"]) | ||
| # Cancel the queued request | ||
| queue_future.cancel() | ||
| time.sleep(2) # ensure the cancellation is delivered | ||
| self._assert_response_is_cancelled(response) | ||
| # Join saturating thread | ||
| saturate_thread_1.result() | ||
| saturate_thread_2.result() | ||
| self._triton.unload_model(model_name) | ||
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| # Test backlogged requests on sequence batch scheduler can be cancelled | ||
| def test_sequence_batch_scheduler_backlog_request_cancellation(self): | ||
| model_name = "sequence_direct" | ||
| self._triton.load_model(model_name) | ||
| with concurrent.futures.ThreadPoolExecutor() as pool: | ||
| # Saturate the single sequence slot | ||
| saturate_thread = pool.submit( | ||
| self._triton.infer, | ||
| model_name, | ||
| self._get_inputs(batch_size=1), | ||
| sequence_id=1, | ||
| sequence_start=True, | ||
| ) | ||
| time.sleep(2) # ensure the slot is filled | ||
| # The next sequence should be on the backlog | ||
| callback, response = self._generate_callback_and_response_pair() | ||
| backlog_future = self._triton.async_infer( | ||
| model_name, | ||
| self._get_inputs(batch_size=1), | ||
| callback, | ||
| sequence_id=2, | ||
| sequence_start=True, | ||
| ) | ||
| time.sleep(2) # ensure the sequence is backlogged | ||
| self.assertFalse(response["responded"]) | ||
| # Cancel the backlog sequence | ||
| backlog_future.cancel() | ||
| time.sleep(2) # ensure the cancellation is delivered | ||
| self._assert_response_is_cancelled(response) | ||
| # Join saturating thread | ||
| saturate_thread.result() | ||
| self._triton.unload_model(model_name) | ||
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| # Test queued requests on direct sequence batch scheduler can be cancelled | ||
| def test_direct_sequence_batch_scheduler_request_cancellation(self): | ||
| model_name = "sequence_direct" | ||
| self._test_sequence_batch_scheduler_queued_request_cancellation(model_name) | ||
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| # Test queued requests on oldest sequence batch scheduler can be cancelled | ||
| def test_oldest_sequence_batch_scheduler_request_cancellation(self): | ||
| model_name = "sequence_oldest" | ||
| self._test_sequence_batch_scheduler_queued_request_cancellation(model_name) | ||
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| # Helper function | ||
| def _test_sequence_batch_scheduler_queued_request_cancellation(self, model_name): | ||
| self._triton.load_model(model_name) | ||
| with concurrent.futures.ThreadPoolExecutor() as pool: | ||
| # Start the sequence | ||
| start_thread = pool.submit( | ||
| self._triton.infer, | ||
| model_name, | ||
| self._get_inputs(batch_size=1), | ||
| sequence_id=1, | ||
| sequence_start=True, | ||
| ) | ||
| # The next request should be queued | ||
| callback, response = self._generate_callback_and_response_pair() | ||
| queue_future = self._triton.async_infer( | ||
| model_name, self._get_inputs(batch_size=1), callback, sequence_id=1 | ||
| ) | ||
| time.sleep(2) # ensure the request is queued | ||
| self.assertFalse(response["responded"]) | ||
| # Cancel the queued request | ||
| queue_future.cancel() | ||
| time.sleep(2) # ensure the cancellation is delivered | ||
| self._assert_response_is_cancelled(response) | ||
| # Join start thread | ||
| start_thread.result() | ||
| self._triton.unload_model(model_name) | ||
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| # Test ensemble scheduler will propagate cancellation request to child | ||
| def test_ensemble_scheduler_request_cancellation(self): | ||
| model_name = "ensemble_model" | ||
| self._triton.load_model(model_name) | ||
| callback, response = self._generate_callback_and_response_pair() | ||
| infer_future = self._triton.async_infer( | ||
| model_name, self._get_inputs(batch_size=1), callback | ||
| ) | ||
| time.sleep(2) # ensure the inference has started | ||
| self.assertFalse(response["responded"]) | ||
| infer_future.cancel() | ||
| time.sleep(2) # ensure the cancellation is delivered | ||
| self._assert_response_is_cancelled(response) | ||
| self._triton.unload_model(model_name, unload_dependents=True) | ||
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| if __name__ == "__main__": | ||
| unittest.main() | ||
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